ArticleStatistics in medicine2025
Joint Latent Class Models: A Tutorial on Practical Applications in Clinical Research.
Article in Statistics in medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
3 citing papers in PubMed.
- Deep learning embedded latent class joint modelling of time-to-event and longitudinal data.Statistical methods in medical research · 2026Article
- Weekly physical activity trajectories in short- and long-term survivors of childhood cancer: the PACCS and SURfit studies.Journal of cancer survivorship : research and practice · 2026Article
- Dynamic prediction of adverse outcomes in discharged COPD patients using patient-reported outcomes.BMC medical research methodology · 2026Observational
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Joint latent class model is a statistical approach allowing to simultaneously account for two outcomes related to disease progression: A longitudinal measure (for example a biomarker) and time-to-event, in the context of a heterogeneous population. Within this approach, the linear mixed model, describing the longitudinal measure, is connected to the survival model, describing the risk of event occurrence, via a model for latent classes, describing an unobserved population heterogeneity; thus, the behavior of the two outcomes is assumed to be specific to each latent class. The theoretical properties of the model are established and the model is implemented in software. However, its complexity makes it difficult to manipulate by clinicians. In this paper, we propose a detailed tutorial for clinicians and applied statisticians on how to specify the model in R software in order to respond to concrete clinical questions, how to explore, manipulate, interpret the provided results. The tutorial is based on a real clinical dataset; for each clinical question the mathematical model specification and the R script for implementation are provided, and the estimation results and goodness-of-fit measures are detailed and interpreted.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.